{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to further train a pre-trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will demonstrate how to freeze some or all of the layers of a pre-trained model and continue training using a new fully-connected set of layers and data with a different format. \n",
    "\n",
    "Adapted from the Tensorflow 2.0 [transfer learning tutorial](https://www.tensorflow.org/tutorials/images/transfer_learning)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:13.610908Z",
     "start_time": "2020-06-21T20:57:11.929681Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from sklearn.datasets import load_files       \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Sequential, Model \n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\n",
    "\n",
    "import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:13.657169Z",
     "start_time": "2020-06-21T20:57:13.612289Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = Path('results', 'transfer_learning')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:13.667092Z",
     "start_time": "2020-06-21T20:57:13.658946Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load TensorFlow Cats vs Dog Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow includes a large number of built-in dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:13.679598Z",
     "start_time": "2020-06-21T20:57:13.668321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abstract_reasoning',\n",
       " 'aflw2k3d',\n",
       " 'amazon_us_reviews',\n",
       " 'bair_robot_pushing_small',\n",
       " 'bigearthnet',\n",
       " 'binarized_mnist',\n",
       " 'binary_alpha_digits',\n",
       " 'caltech101',\n",
       " 'caltech_birds2010',\n",
       " 'caltech_birds2011',\n",
       " 'cats_vs_dogs',\n",
       " 'celeb_a',\n",
       " 'celeb_a_hq',\n",
       " 'chexpert',\n",
       " 'cifar10',\n",
       " 'cifar100',\n",
       " 'cifar10_corrupted',\n",
       " 'clevr',\n",
       " 'cnn_dailymail',\n",
       " 'coco',\n",
       " 'coco2014',\n",
       " 'coil100',\n",
       " 'colorectal_histology',\n",
       " 'colorectal_histology_large',\n",
       " 'curated_breast_imaging_ddsm',\n",
       " 'cycle_gan',\n",
       " 'deep_weeds',\n",
       " 'definite_pronoun_resolution',\n",
       " 'diabetic_retinopathy_detection',\n",
       " 'downsampled_imagenet',\n",
       " 'dsprites',\n",
       " 'dtd',\n",
       " 'dummy_dataset_shared_generator',\n",
       " 'dummy_mnist',\n",
       " 'emnist',\n",
       " 'eurosat',\n",
       " 'fashion_mnist',\n",
       " 'flores',\n",
       " 'food101',\n",
       " 'gap',\n",
       " 'glue',\n",
       " 'groove',\n",
       " 'higgs',\n",
       " 'horses_or_humans',\n",
       " 'image_label_folder',\n",
       " 'imagenet2012',\n",
       " 'imagenet2012_corrupted',\n",
       " 'imdb_reviews',\n",
       " 'iris',\n",
       " 'kitti',\n",
       " 'kmnist',\n",
       " 'lfw',\n",
       " 'lm1b',\n",
       " 'lsun',\n",
       " 'mnist',\n",
       " 'mnist_corrupted',\n",
       " 'moving_mnist',\n",
       " 'multi_nli',\n",
       " 'nsynth',\n",
       " 'omniglot',\n",
       " 'open_images_v4',\n",
       " 'oxford_flowers102',\n",
       " 'oxford_iiit_pet',\n",
       " 'para_crawl',\n",
       " 'patch_camelyon',\n",
       " 'pet_finder',\n",
       " 'quickdraw_bitmap',\n",
       " 'resisc45',\n",
       " 'rock_paper_scissors',\n",
       " 'rock_you',\n",
       " 'scene_parse150',\n",
       " 'shapes3d',\n",
       " 'smallnorb',\n",
       " 'snli',\n",
       " 'so2sat',\n",
       " 'squad',\n",
       " 'stanford_dogs',\n",
       " 'stanford_online_products',\n",
       " 'starcraft_video',\n",
       " 'sun397',\n",
       " 'super_glue',\n",
       " 'svhn_cropped',\n",
       " 'ted_hrlr_translate',\n",
       " 'ted_multi_translate',\n",
       " 'tf_flowers',\n",
       " 'titanic',\n",
       " 'trivia_qa',\n",
       " 'uc_merced',\n",
       " 'ucf101',\n",
       " 'visual_domain_decathlon',\n",
       " 'voc2007',\n",
       " 'wikipedia',\n",
       " 'wmt14_translate',\n",
       " 'wmt15_translate',\n",
       " 'wmt16_translate',\n",
       " 'wmt17_translate',\n",
       " 'wmt18_translate',\n",
       " 'wmt19_translate',\n",
       " 'wmt_t2t_translate',\n",
       " 'wmt_translate',\n",
       " 'xnli']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfds.list_builders()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a set of cats and dog images for binary classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.027806Z",
     "start_time": "2020-06-21T20:57:13.681486Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "(raw_train, raw_validation, raw_test), metadata = tfds.load(\n",
    "    'cats_vs_dogs',\n",
    "    split=[\n",
    "        tfds.Split.TRAIN.subsplit(tfds.percent[:80]),\n",
    "        tfds.Split.TRAIN.subsplit(tfds.percent[80:90]),\n",
    "        tfds.Split.TRAIN.subsplit(tfds.percent[90:])\n",
    "    ],\n",
    "    with_info=True,\n",
    "    as_supervised=True,\n",
    "    data_dir='../data/tensorflow'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.032844Z",
     "start_time": "2020-06-21T20:57:14.029165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw train:\t <_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n",
      "Raw validation:\t <_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n",
      "Raw test:\t <_OptionsDataset shapes: ((None, None, 3), ()), types: (tf.uint8, tf.int64)>\n"
     ]
    }
   ],
   "source": [
    "print('Raw train:\\t', raw_train)\n",
    "print('Raw validation:\\t', raw_validation)\n",
    "print('Raw test:\\t', raw_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show sample images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.603947Z",
     "start_time": "2020-06-21T20:57:14.035302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_label_name = metadata.features['label'].int2str\n",
    "\n",
    "for image, label in raw_train.take(2):\n",
    "    plt.figure()\n",
    "    plt.imshow(image)\n",
    "    plt.title(get_label_name(label))\n",
    "    plt.grid(False)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All images will be resized to 160x160:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.608054Z",
     "start_time": "2020-06-21T20:57:14.605666Z"
    }
   },
   "outputs": [],
   "source": [
    "IMG_SIZE = 160\n",
    "IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.618887Z",
     "start_time": "2020-06-21T20:57:14.609502Z"
    }
   },
   "outputs": [],
   "source": [
    "def format_example(image, label):\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    image = (image/127.5) - 1\n",
    "    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.702143Z",
     "start_time": "2020-06-21T20:57:14.620290Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "train = raw_train.map(format_example)\n",
    "validation = raw_validation.map(format_example)\n",
    "test = raw_test.map(format_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.705126Z",
     "start_time": "2020-06-21T20:57:14.703338Z"
    }
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "SHUFFLE_BUFFER_SIZE = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:14.714773Z",
     "start_time": "2020-06-21T20:57:14.706160Z"
    }
   },
   "outputs": [],
   "source": [
    "train_batches = train.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
    "validation_batches = validation.batch(BATCH_SIZE)\n",
    "test_batches = test.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:15.720279Z",
     "start_time": "2020-06-21T20:57:14.715895Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 160, 160, 3])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for image_batch, label_batch in train_batches.take(1):\n",
    "    pass\n",
    "\n",
    "image_batch.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the VGG-16 Bottleneck Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the VGG16 weights, pre-trained on ImageNet with the much smaller 32 x 32 CIFAR10 data. Note that we indicate the new input size upon import and set all layers to not trainable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:16.756259Z",
     "start_time": "2020-06-21T20:57:15.721419Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 160, 160, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 160, 160, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 160, 160, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 80, 80, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 80, 80, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 80, 80, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 40, 40, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 40, 40, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 20, 20, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 20, 20, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 10, 10, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 5, 5, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg16 = VGG16(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')\n",
    "vgg16.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.408060Z",
     "start_time": "2020-06-21T20:57:16.757697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 5, 5, 512])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_batch = vgg16(image_batch)\n",
    "feature_batch.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Freeze model layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.412402Z",
     "start_time": "2020-06-21T20:57:18.409455Z"
    }
   },
   "outputs": [],
   "source": [
    "vgg16.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.430176Z",
     "start_time": "2020-06-21T20:57:18.413779Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 160, 160, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 160, 160, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 160, 160, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 80, 80, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 80, 80, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 80, 80, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 40, 40, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 40, 40, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 20, 20, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 20, 20, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 10, 10, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 5, 5, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg16.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add new layers to model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Sequential model API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.439436Z",
     "start_time": "2020-06-21T20:57:18.432248Z"
    }
   },
   "outputs": [],
   "source": [
    "global_average_layer = GlobalAveragePooling2D()\n",
    "dense_layer = Dense(64, activation='relu')\n",
    "dropout = Dropout(0.5)\n",
    "prediction_layer = Dense(1, activation='sigmoid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.528797Z",
     "start_time": "2020-06-21T20:57:18.441095Z"
    }
   },
   "outputs": [],
   "source": [
    "seq_model = tf.keras.Sequential([vgg16, \n",
    "                                 global_average_layer, \n",
    "                                 dense_layer, \n",
    "                                 dropout, \n",
    "                                 prediction_layer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.542257Z",
     "start_time": "2020-06-21T20:57:18.529840Z"
    }
   },
   "outputs": [],
   "source": [
    "seq_model.compile(loss = tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
    "                       optimizer = 'Adam', \n",
    "                       metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.555837Z",
     "start_time": "2020-06-21T20:57:18.543582Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 5, 5, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                32832     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 14,747,585\n",
      "Trainable params: 32,897\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "seq_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Functional model API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use Keras’ functional API to define the vgg16 output as input into a new set of fully-connected layers like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.588845Z",
     "start_time": "2020-06-21T20:57:18.559671Z"
    }
   },
   "outputs": [],
   "source": [
    "#Adding custom Layers \n",
    "x = vgg16.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(1, activation='sigmoid')(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a new model in terms of inputs and output, and proceed from there on as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.595662Z",
     "start_time": "2020-06-21T20:57:18.590115Z"
    }
   },
   "outputs": [],
   "source": [
    "transfer_model = Model(inputs = vgg16.input, \n",
    "                       outputs = predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.608940Z",
     "start_time": "2020-06-21T20:57:18.597423Z"
    }
   },
   "outputs": [],
   "source": [
    "transfer_model.compile(loss = tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
    "                       optimizer = 'Adam', \n",
    "                       metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:18.622719Z",
     "start_time": "2020-06-21T20:57:18.610451Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 160, 160, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 160, 160, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 160, 160, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 80, 80, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 80, 80, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 80, 80, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 40, 40, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 40, 40, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 20, 20, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 20, 20, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 10, 10, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 5, 5, 512)         0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                32832     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 14,747,585\n",
      "Trainable params: 32,897\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "transfer_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute baseline metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:19.923450Z",
     "start_time": "2020-06-21T20:57:18.624102Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 42ms/step - loss: 0.7171 - accuracy: 0.5141\n"
     ]
    }
   ],
   "source": [
    "initial_epochs = 10\n",
    "validation_steps=20\n",
    "\n",
    "initial_loss, initial_accuracy = transfer_model.evaluate(validation_batches, steps = validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T20:57:59.898154Z",
     "start_time": "2020-06-21T20:57:59.895814Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss: 0.72 | initial_accuracy accuracy: 51.41%\n"
     ]
    }
   ],
   "source": [
    "print(f'Initial loss: {initial_loss:.2f} | initial_accuracy accuracy: {initial_accuracy:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train VGG16 transfer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:45.602594Z",
     "start_time": "2020-06-21T20:58:01.534921Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "582/582 [==============================] - 45s 78ms/step - loss: 0.5661 - accuracy: 0.8697 - val_loss: 0.5488 - val_accuracy: 0.9358\n",
      "Epoch 2/10\n",
      "582/582 [==============================] - 45s 77ms/step - loss: 0.5386 - accuracy: 0.9269 - val_loss: 0.5417 - val_accuracy: 0.9272\n",
      "Epoch 3/10\n",
      "582/582 [==============================] - 44s 76ms/step - loss: 0.5349 - accuracy: 0.9326 - val_loss: 0.5398 - val_accuracy: 0.9409\n",
      "Epoch 4/10\n",
      "582/582 [==============================] - 45s 77ms/step - loss: 0.5334 - accuracy: 0.9350 - val_loss: 0.5470 - val_accuracy: 0.9366\n",
      "Epoch 5/10\n",
      "582/582 [==============================] - 45s 78ms/step - loss: 0.5320 - accuracy: 0.9379 - val_loss: 0.5456 - val_accuracy: 0.9392\n",
      "Epoch 6/10\n",
      "582/582 [==============================] - 44s 76ms/step - loss: 0.5319 - accuracy: 0.9366 - val_loss: 0.5382 - val_accuracy: 0.9422\n",
      "Epoch 7/10\n",
      "582/582 [==============================] - 45s 78ms/step - loss: 0.5302 - accuracy: 0.9408 - val_loss: 0.5413 - val_accuracy: 0.9422\n",
      "Epoch 8/10\n",
      "582/582 [==============================] - 46s 79ms/step - loss: 0.5311 - accuracy: 0.9385 - val_loss: 0.5384 - val_accuracy: 0.9457\n",
      "Epoch 9/10\n",
      "582/582 [==============================] - 47s 81ms/step - loss: 0.5298 - accuracy: 0.9420 - val_loss: 0.5383 - val_accuracy: 0.9453\n",
      "Epoch 10/10\n",
      "582/582 [==============================] - 46s 79ms/step - loss: 0.5281 - accuracy: 0.9466 - val_loss: 0.5369 - val_accuracy: 0.9461\n"
     ]
    }
   ],
   "source": [
    "history = transfer_model.fit(train_batches,\n",
    "                             epochs=initial_epochs,\n",
    "                             validation_data=validation_batches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Learning Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:45.613404Z",
     "start_time": "2020-06-21T21:05:45.606918Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_learning_curves(df):\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(15, 4))\n",
    "    df[['loss', 'val_loss']].plot(ax=axes[0], title='Cross-Entropy')\n",
    "    df[['accuracy', 'val_accuracy']].plot(ax=axes[1], title='Accuracy')\n",
    "    for ax in axes:\n",
    "        ax.legend(['Training', 'Validation'])\n",
    "    sns.despine()        \n",
    "    fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:45.631307Z",
     "start_time": "2020-06-21T21:05:45.616310Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics = pd.DataFrame(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:46.148785Z",
     "start_time": "2020-06-21T21:05:45.633348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tune VGG16 weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfreeze selected layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:46.152973Z",
     "start_time": "2020-06-21T21:05:46.150163Z"
    }
   },
   "outputs": [],
   "source": [
    "vgg16.trainable = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-22T15:52:01.000426Z",
     "start_time": "2020-03-22T15:52:00.997549Z"
    }
   },
   "source": [
    "How many layers are in the base model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:46.179991Z",
     "start_time": "2020-06-21T21:05:46.154333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Number of layers in the base model: 19'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'Number of layers in the base model: {len(vgg16.layers)}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:46.188602Z",
     "start_time": "2020-06-21T21:05:46.182729Z"
    }
   },
   "outputs": [],
   "source": [
    "# Fine-tune from this layer onwards\n",
    "start_fine_tuning_at = 12\n",
    "\n",
    "# Freeze all the layers before the `fine_tune_at` layer\n",
    "for layer in vgg16.layers[:start_fine_tuning_at]:\n",
    "    layer.trainable =  False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:46.205205Z",
     "start_time": "2020-06-21T21:05:46.191966Z"
    }
   },
   "outputs": [],
   "source": [
    "base_learning_rate = 0.0001\n",
    "transfer_model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate / 10),\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:46.211347Z",
     "start_time": "2020-06-21T21:05:46.207266Z"
    }
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_accuracy', patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:05:46.232380Z",
     "start_time": "2020-06-21T21:05:46.213121Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 160, 160, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 160, 160, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 160, 160, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 80, 80, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 80, 80, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 80, 80, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 40, 40, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 40, 40, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 40, 40, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 20, 20, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 20, 20, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 10, 10, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 5, 5, 512)         0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                32832     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 14,747,585\n",
      "Trainable params: 11,831,937\n",
      "Non-trainable params: 2,915,648\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "transfer_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continue Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we proceed to train the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:29:04.726934Z",
     "start_time": "2020-06-21T21:05:46.235625Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/60\n",
      "582/582 [==============================] - 58s 100ms/step - loss: 0.5281 - accuracy: 0.9465 - val_loss: 0.5305 - val_accuracy: 0.9539\n",
      "Epoch 11/60\n",
      "582/582 [==============================] - 57s 98ms/step - loss: 0.5200 - accuracy: 0.9640 - val_loss: 0.5269 - val_accuracy: 0.9629\n",
      "Epoch 12/60\n",
      "582/582 [==============================] - 58s 99ms/step - loss: 0.5159 - accuracy: 0.9733 - val_loss: 0.5283 - val_accuracy: 0.9677\n",
      "Epoch 13/60\n",
      "582/582 [==============================] - 55s 95ms/step - loss: 0.5138 - accuracy: 0.9774 - val_loss: 0.5281 - val_accuracy: 0.9569\n",
      "Epoch 14/60\n",
      "582/582 [==============================] - 59s 101ms/step - loss: 0.5113 - accuracy: 0.9830 - val_loss: 0.5322 - val_accuracy: 0.9651\n",
      "Epoch 15/60\n",
      "582/582 [==============================] - 58s 99ms/step - loss: 0.5102 - accuracy: 0.9857 - val_loss: 0.5231 - val_accuracy: 0.9767\n",
      "Epoch 16/60\n",
      "582/582 [==============================] - 56s 97ms/step - loss: 0.5092 - accuracy: 0.9875 - val_loss: 0.5235 - val_accuracy: 0.9724\n",
      "Epoch 17/60\n",
      "582/582 [==============================] - 58s 99ms/step - loss: 0.5077 - accuracy: 0.9909 - val_loss: 0.5226 - val_accuracy: 0.9759\n",
      "Epoch 18/60\n",
      "582/582 [==============================] - 57s 97ms/step - loss: 0.5075 - accuracy: 0.9916 - val_loss: 0.5232 - val_accuracy: 0.9767\n",
      "Epoch 19/60\n",
      "582/582 [==============================] - 57s 98ms/step - loss: 0.5068 - accuracy: 0.9933 - val_loss: 0.5229 - val_accuracy: 0.9763\n",
      "Epoch 20/60\n",
      "582/582 [==============================] - 56s 96ms/step - loss: 0.5065 - accuracy: 0.9936 - val_loss: 0.5220 - val_accuracy: 0.9763\n",
      "Epoch 21/60\n",
      "582/582 [==============================] - 69s 118ms/step - loss: 0.5065 - accuracy: 0.9937 - val_loss: 0.5270 - val_accuracy: 0.9720\n",
      "Epoch 22/60\n",
      "582/582 [==============================] - 72s 125ms/step - loss: 0.5059 - accuracy: 0.9951 - val_loss: 0.5203 - val_accuracy: 0.9797\n",
      "Epoch 23/60\n",
      "582/582 [==============================] - 67s 115ms/step - loss: 0.5054 - accuracy: 0.9959 - val_loss: 0.5259 - val_accuracy: 0.9724\n",
      "Epoch 24/60\n",
      "582/582 [==============================] - 66s 114ms/step - loss: 0.5058 - accuracy: 0.9951 - val_loss: 0.5210 - val_accuracy: 0.9797\n",
      "Epoch 25/60\n",
      "582/582 [==============================] - 68s 116ms/step - loss: 0.5056 - accuracy: 0.9953 - val_loss: 0.5216 - val_accuracy: 0.9780\n",
      "Epoch 26/60\n",
      "582/582 [==============================] - 57s 98ms/step - loss: 0.5053 - accuracy: 0.9959 - val_loss: 0.5226 - val_accuracy: 0.9759\n",
      "Epoch 27/60\n",
      "582/582 [==============================] - 58s 99ms/step - loss: 0.5050 - accuracy: 0.9968 - val_loss: 0.5234 - val_accuracy: 0.9776\n",
      "Epoch 28/60\n",
      "582/582 [==============================] - 56s 96ms/step - loss: 0.5051 - accuracy: 0.9965 - val_loss: 0.5233 - val_accuracy: 0.9733\n",
      "Epoch 29/60\n",
      "582/582 [==============================] - 57s 98ms/step - loss: 0.5050 - accuracy: 0.9969 - val_loss: 0.5239 - val_accuracy: 0.9759\n",
      "Epoch 30/60\n",
      "582/582 [==============================] - 57s 99ms/step - loss: 0.5047 - accuracy: 0.9971 - val_loss: 0.5230 - val_accuracy: 0.9780\n",
      "Epoch 31/60\n",
      "582/582 [==============================] - 57s 99ms/step - loss: 0.5047 - accuracy: 0.9974 - val_loss: 0.5224 - val_accuracy: 0.9772\n",
      "Epoch 32/60\n",
      "582/582 [==============================] - 58s 99ms/step - loss: 0.5047 - accuracy: 0.9975 - val_loss: 0.5250 - val_accuracy: 0.9733\n"
     ]
    }
   ],
   "source": [
    "fine_tune_epochs = 50\n",
    "total_epochs = initial_epochs + fine_tune_epochs\n",
    "\n",
    "history_fine_tune = transfer_model.fit(train_batches,\n",
    "                                       epochs=total_epochs,\n",
    "                                       initial_epoch=history.epoch[-1],\n",
    "                                       validation_data=validation_batches,\n",
    "                                       callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:29:04.734069Z",
     "start_time": "2020-06-21T21:29:04.728818Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics_tuned = metrics.append(pd.DataFrame(history_fine_tune.history), ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:29:05.348536Z",
     "start_time": "2020-06-21T21:29:04.735290Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(15, 4))\n",
    "metrics_tuned[['loss', 'val_loss']].plot(ax=axes[1], title='Cross-Entropy Loss')\n",
    "metrics_tuned[['accuracy', 'val_accuracy']].plot(ax=axes[0], title=f'Accuracy (Best: {metrics_tuned.val_accuracy.max():.2%})')\n",
    "axes[0].yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y)))\n",
    "axes[0].set_ylabel('Accuracy')\n",
    "axes[1].set_ylabel('Loss')\n",
    "for ax in axes:\n",
    "    ax.axvline(10, ls='--', lw=1, c='k')\n",
    "    ax.legend(['Training', 'Validation', 'Start Fine Tuning'])\n",
    "    ax.set_xlabel('Epoch')\n",
    "sns.despine()    \n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'transfer_learning');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml4t-dl]",
   "language": "python",
   "name": "conda-env-ml4t-dl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "318.55px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}